{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "whjsJasuhstV"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/jeffheaton/app_generative_ai/blob/main/t81_559_class_02_3_llm_debug.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "euOZxlIMhstX"
   },
   "source": [
    "# T81-559: Applications of Generative Artificial Intelligence\n",
    "**Module 2: Code Generation**\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d4Yov72PhstY"
   },
   "source": [
    "# Module 2 Material\n",
    "\n",
    "* Part 2.1: Prompting for Code Generation [[Video]](https://www.youtube.com/watch?v=HVId6kYKKgQ) [[Notebook]](t81_559_class_02_1_dev.ipynb)\n",
    "* Part 2.2: Handling Revision Prompts [[Video]](https://www.youtube.com/watch?v=APpV46tplXA) [[Notebook]](t81_559_class_02_2_multi_prompt.ipynb)\n",
    "* **Part 2.3: Using a LLM to Help Debug** [[Video]](https://www.youtube.com/watch?v=VPqSNb38QK0) [[Notebook]](t81_559_class_02_3_llm_debug.ipynb)\n",
    "* Part 2.4: Tracking Prompts in Software Development [[Video]](https://www.youtube.com/watch?v=oUFUuYfvXZU) [[Notebook]](t81_559_class_02_4_software_eng.ipynb)\n",
    "* Part 2.5: Limits of LLM Code Generation [[Video]](https://www.youtube.com/watch?v=dKtRI0LZSyY) [[Notebook]](t81_559_class_02_5_code_gen_limits.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AcAUP0c3hstY"
   },
   "source": [
    "# Google CoLab Instructions\n",
    "\n",
    "The following code ensures that Google CoLab is running and maps Google Drive if needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xsI496h5hstZ",
    "outputId": "ff9d9f55-f0a6-4c19-8f50-e26c60a318a1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: using Google CoLab\n",
      "Requirement already satisfied: langchain in /usr/local/lib/python3.10/dist-packages (0.1.16)\n",
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      "Requirement already satisfied: langchain-community<0.1,>=0.0.32 in /usr/local/lib/python3.10/dist-packages (from langchain) (0.0.34)\n",
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      "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->openai<2.0.0,>=1.10.0->langchain_openai) (1.2.1)\n",
      "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->openai<2.0.0,>=1.10.0->langchain_openai) (1.0.5)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai<2.0.0,>=1.10.0->langchain_openai) (0.14.0)\n",
      "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain) (1.0.0)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "try:\n",
    "    from google.colab import drive, userdata\n",
    "    COLAB = True\n",
    "    print(\"Note: using Google CoLab\")\n",
    "except:\n",
    "    print(\"Note: not using Google CoLab\")\n",
    "    COLAB = False\n",
    "\n",
    "# OpenAI Secrets\n",
    "if COLAB:\n",
    "    os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')\n",
    "\n",
    "# Install needed libraries in CoLab\n",
    "if COLAB:\n",
    "    !pip install langchain langchain_openai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pC9A-LaYhsta"
   },
   "source": [
    "# 2.3: Using a LLM to Help Debug\n",
    "\n",
    "LLMs can help you debug both the code you create and the code you generate to fulfill your requests. In this part, you will see how to use an LLM as an assistant to help debug a Python program.\n",
    "\n",
    "## Conversational Code Generation\n",
    "\n",
    "We will continue to use the conversational code generation function provided in Module 2.2.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "TMF-rtxgRAea"
   },
   "outputs": [],
   "source": [
    "from langchain.chains import ConversationChain\n",
    "from langchain.memory import ConversationBufferWindowMemory\n",
    "from langchain_openai import ChatOpenAI\n",
    "from langchain_core.prompts.chat import PromptTemplate\n",
    "from IPython.display import display_markdown\n",
    "\n",
    "MODEL = 'gpt-4o-mini'\n",
    "TEMPLATE = \"\"\"The following is a friendly conversation between a human and an\n",
    "AI to generate Python code. If you have notes about the code, place them before\n",
    "the code. Any nots about execution should follow the code. If you do mix any\n",
    "notes with the code, make them comments. Add proper comments to the code.\n",
    "Sort imports and follow PEP-8 formatting.\n",
    "\n",
    "Current conversation:\n",
    "{history}\n",
    "Human: {input}\n",
    "Code Assistant:\"\"\"\n",
    "PROMPT_TEMPLATE = PromptTemplate(input_variables=[\"history\", \"input\"], template=TEMPLATE)\n",
    "\n",
    "def start_conversation():\n",
    "    # Initialize the OpenAI LLM with your API key\n",
    "    llm = ChatOpenAI(\n",
    "        model=MODEL,\n",
    "        temperature=0.0,\n",
    "        n=1\n",
    "    )\n",
    "\n",
    "    # Initialize memory and conversation\n",
    "    memory = ConversationBufferWindowMemory()\n",
    "    conversation = ConversationChain(\n",
    "        prompt=PROMPT_TEMPLATE,\n",
    "        llm=llm,\n",
    "        memory=memory,\n",
    "        verbose=False\n",
    "    )\n",
    "\n",
    "    return conversation\n",
    "\n",
    "def generate_code(conversation, prompt):\n",
    "    print(\"Model response:\")\n",
    "    output = conversation.invoke(prompt)\n",
    "    display_markdown(output['response'], raw=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ClPhLkGldhPt"
   },
   "source": [
    "## A Buggy Pi Approximator\n",
    "\n",
    "To see an example of how you can make use of LLM-enabled debugging, consider the following code to use the [Monte Carlo](https://en.wikipedia.org/wiki/Monte_Carlo_method) method to estimate [Pi](https://en.wikipedia.org/wiki/Pi). We need to fix several issues with this code. We can request the LLM to help us debug. This code, when executed, produces the following error:\n",
    "\n",
    "```\n",
    "NameError: name 'xrange' is not defined\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "id": "ydaqwgRiH4D6",
    "outputId": "05467ef0-f0b2-423d-bc06-8a46ce81390c"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'xrange' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-c7b4356f1718>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# Example usage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mnum_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1000000\u001b[0m  \u001b[0;31m# Number of random points to generate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mapproximated_pi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmonte_carlo_pi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_samples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Approximated Pi with {num_samples} samples: {approximated_pi}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-4-c7b4356f1718>\u001b[0m in \u001b[0;36mmonte_carlo_pi\u001b[0;34m(num_samples)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0minside_circle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_samples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Generate random point (x, y)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'xrange' is not defined"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "def monte_carlo_pi(num_samples):\n",
    "    inside_circle = 0\n",
    "\n",
    "    for _ in xrange(num_samples):\n",
    "        x, y = random.random(), random.random()  # Generate random point (x, y)\n",
    "        if x*2 + y*2 <= 1:\n",
    "            inside_circle += 1  # Check if the point is inside the quarter circle\n",
    "\n",
    "    pi_approximation = 4 * inside_circle / num_samples  # Calculate approximation of Pi\n",
    "    return pi_approximation\n",
    "\n",
    "# Example usage\n",
    "num_samples = 1000000  # Number of random points to generate\n",
    "approximated_pi = monte_carlo_pi(num_samples)\n",
    "print(f\"Approximated Pi with {num_samples} samples: {approximated_pi}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jKC-7gT-caaQ"
   },
   "source": [
    "When we ask the LLM to help us debug this code, we should provide as much detail as possible. I usually like to produce a prompt in the following format:\n",
    "\n",
    "```\n",
    "I am trying to debug the following code:\n",
    "\n",
    "... provide code here...\n",
    "\n",
    "However, I am getting the following error:\n",
    "\n",
    "... add the error here, provide stack trace ...\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 618
    },
    "id": "lqnDZhc4OVU6",
    "outputId": "ad8a5f5a-87ba-48f5-adac-a89729d3dd48"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model response:\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "It looks like you're using Python 3, where `xrange` has been replaced by `range`. Also, there's a mistake in the condition to check if the point is inside the quarter circle. It should be `x**2 + y**2 <= 1` instead of `x*2 + y*2 <= 1`. Here's the corrected version of your code:\n",
       "\n",
       "```python\n",
       "import random\n",
       "\n",
       "def monte_carlo_pi(num_samples):\n",
       "    \"\"\"\n",
       "    Estimate the value of Pi using the Monte Carlo method.\n",
       "    \n",
       "    Args:\n",
       "    num_samples (int): Number of random samples to generate.\n",
       "    \n",
       "    Returns:\n",
       "    float: Approximated value of Pi.\n",
       "    \"\"\"\n",
       "    inside_circle = 0\n",
       "    \n",
       "    for _ in range(num_samples):  # Use range instead of xrange for Python 3\n",
       "        x, y = random.random(), random.random()  # Generate random point (x, y)\n",
       "        if x**2 + y**2 <= 1:  # Correct formula to check if inside the quarter circle\n",
       "            inside_circle += 1\n",
       "    \n",
       "    pi_approximation = 4 * inside_circle / num_samples  # Calculate approximation of Pi\n",
       "    return pi_approximation\n",
       "\n",
       "# Example usage\n",
       "num_samples = 1000000  # Number of random points to generate\n",
       "approximated_pi = monte_carlo_pi(num_samples)\n",
       "print(f\"Approximated Pi with {num_samples} samples: {approximated_pi}\")\n",
       "```\n",
       "\n",
       "### Execution Notes:\n",
       "- Ensure you are running this script in a Python 3 environment.\n",
       "- The number of samples (`num_samples`) can be adjusted to see how the approximation improves with more samples."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conversation = start_conversation()\n",
    "generate_code(conversation, \"\"\"\n",
    "I am trying to debug the following code:\n",
    "\n",
    "import random\n",
    "\n",
    "def monte_carlo_pi(num_samples):\n",
    "    inside_circle = 0\n",
    "\n",
    "    for _ in xrange(num_samples):\n",
    "        x, y = random.random(), random.random()  # Generate random point (x, y)\n",
    "        if x*2 + y*2 <= 1:\n",
    "            inside_circle += 1  # Check if the point is inside the quarter circle\n",
    "\n",
    "    pi_approximation = 4 * inside_circle / num_samples  # Calculate approximation of Pi\n",
    "    return pi_approximation\n",
    "\n",
    "# Example usage\n",
    "num_samples = 1000000  # Number of random points to generate\n",
    "approximated_pi = monte_carlo_pi(num_samples)\n",
    "print(f\"Approximated Pi with {num_samples} samples: {approximated_pi}\")\n",
    "\n",
    "However, I am getting the following error:\n",
    "\n",
    "---------------------------------------------------------------------------\n",
    "NameError                                 Traceback (most recent call last)\n",
    "<ipython-input-10-c7b4356f1718> in <cell line: 16>()\n",
    "     14 # Example usage\n",
    "     15 num_samples = 1000000  # Number of random points to generate\n",
    "---> 16 approximated_pi = monte_carlo_pi(num_samples)\n",
    "     17 print(f\"Approximated Pi with {num_samples} samples: {approximated_pi}\")\n",
    "\n",
    "<ipython-input-10-c7b4356f1718> in monte_carlo_pi(num_samples)\n",
    "      4     inside_circle = 0\n",
    "      5\n",
    "----> 6     for _ in xrange(num_samples):\n",
    "      7         x, y = random.random(), random.random()  # Generate random point (x, y)\n",
    "      8         if x*2 + y*2 <= 1:\n",
    "\n",
    "NameError: name 'xrange' is not defined\n",
    "\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WbqC15eQecdx"
   },
   "source": [
    "In this case, the LLM decided to be an overachiever because I only asked it about the specific error I was getting. However, the LLM provided me with two issues, one of which was the error I encountered. The LLM identified these two issues:\n",
    "\n",
    "* It looks like you're using Python 3, where xrange has been replaced by range.\n",
    "\n",
    "* Also, there's a mistake in the condition to check if the point is inside the quarter circle. It should be ```x ** 2 + y ** 2 <= 1``` instead ```of x*2 + y*2 <= 1```.\n",
    "\n",
    "The LLM also provided a corrected code for me to copy/paste."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C7U1Bx3Wbje1"
   },
   "source": [
    "## Testing the Corrected Code\n",
    "\n",
    "Now, we can test the corrected code and see that it works properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CdJNKLVhRcD3",
    "outputId": "6774fa8f-ddf5-4168-d6dc-6956d799140c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximated Pi with 1000000 samples: 3.142944\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "def monte_carlo_pi(num_samples):\n",
    "    \"\"\"\n",
    "    Estimate the value of Pi using the Monte Carlo method.\n",
    "\n",
    "    Args:\n",
    "    num_samples (int): Number of random samples to generate.\n",
    "\n",
    "    Returns:\n",
    "    float: Approximated value of Pi.\n",
    "    \"\"\"\n",
    "    inside_circle = 0\n",
    "\n",
    "    for _ in range(num_samples):  # Use range instead of xrange for Python 3\n",
    "        x, y = random.random(), random.random()  # Generate random point (x, y)\n",
    "        if x**2 + y**2 <= 1:  # Correct formula to check if inside the quarter circle\n",
    "            inside_circle += 1\n",
    "\n",
    "    pi_approximation = 4 * inside_circle / num_samples  # Calculate approximation of Pi\n",
    "    return pi_approximation\n",
    "\n",
    "# Example usage\n",
    "num_samples = 1000000  # Number of random points to generate\n",
    "approximated_pi = monte_carlo_pi(num_samples)\n",
    "print(f\"Approximated Pi with {num_samples} samples: {approximated_pi}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mAp8eHrFfVIi"
   },
   "source": [
    "## LLMs Explaining Code\n",
    "\n",
    "LLMs are also very adept at explaining code. As you work through this course, you will see that the assignments use a submission function I named \"submit.\" This submission function uses HTTP and API calling techniques that are not covered by this course. However, if you are interested in what the \"submit\" function does, you can ask the LLM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "AsYB3VFkfdi7",
    "outputId": "0aebf39a-6932-403f-da7b-8db890708ae9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model response:\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "The provided Python function `submit` is designed to handle the submission of assignments, which can include both data frames and images. It also requires the source file of the submission to be encoded and sent as part of the request. Below are detailed notes on what each part of the code does, followed by the code itself with added comments for clarity.\n",
       "\n",
       "### Notes on the Code:\n",
       "1. **Imports**: The code uses several libraries. `base64` for encoding files, `os` for operating system interface, `numpy` and `pandas` for data handling, `requests` for making HTTP requests, `PIL` for image processing, and `io` for handling byte streams.\n",
       "2. **Function Definition**: The function `submit` takes four parameters: `data`, `key`, `no`, and `source_file`. It is designed to submit assignments which can be either a list of pandas dataframes or images.\n",
       "3. **Source File Validation**: It checks if the source file is provided or infers it using the `__file__` global variable. It also validates that the filename contains a specific suffix related to the assignment number.\n",
       "4. **Encoding the Source File**: The source file (Python script or Jupyter notebook) is read and encoded in base64 format.\n",
       "5. **Data Handling**: The function iterates over the `data` list, checking the type of each item. Images are converted to PNG format and encoded in base64, while data frames are converted to CSV format and similarly encoded.\n",
       "6. **HTTP Request**: It constructs a payload with the encoded data and metadata, then sends a POST request to a specified URL.\n",
       "7. **Response Handling**: After sending the request, the function checks the response status and prints out a success or failure message based on the status code received.\n",
       "\n",
       "### Python Code with Comments:\n",
       "```python\n",
       "# Import necessary libraries\n",
       "import base64\n",
       "import io\n",
       "import os\n",
       "import pandas as pd\n",
       "import PIL.Image\n",
       "import requests\n",
       "\n",
       "# Function to submit assignments\n",
       "def submit(data, key, no, source_file=None):\n",
       "    # Validate or set the source_file parameter\n",
       "    if source_file is None and '__file__' not in globals():\n",
       "        raise Exception('Must specify a filename when using a Jupyter notebook.')\n",
       "    if source_file is None:\n",
       "        source_file = __file__\n",
       "    \n",
       "    # Check if the filename contains the correct class suffix\n",
       "    suffix = '_class{}'.format(no)\n",
       "    if suffix not in source_file:\n",
       "        raise Exception('{} must be part of the filename.'.format(suffix))\n",
       "    \n",
       "    # Read and encode the source file\n",
       "    with open(source_file, \"rb\") as image_file:\n",
       "        encoded_python = base64.b64encode(image_file.read()).decode('ascii')\n",
       "    \n",
       "    # Validate the file extension\n",
       "    ext = os.path.splitext(source_file)[-1].lower()\n",
       "    if ext not in ['.ipynb', '.py']:\n",
       "        raise Exception(\"Source file is {} must be .py or .ipynb\".format(ext))\n",
       "    \n",
       "    # Prepare the payload with encoded data\n",
       "    payload = []\n",
       "    for item in data:\n",
       "        if isinstance(item, PIL.Image.Image):\n",
       "            buffered = io.BytesIO()\n",
       "            item.save(buffered, format=\"PNG\")\n",
       "            payload.append({'PNG': base64.b64encode(buffered.getvalue()).decode('ascii')})\n",
       "        elif isinstance(item, pd.DataFrame):\n",
       "            payload.append({'CSV': base64.b64encode(item.to_csv(index=False).encode('ascii')).decode(\"ascii\")})\n",
       "    \n",
       "    # Send the POST request\n",
       "    r = requests.post(\"https://api.heatonresearch.com/assignment-submit\",\n",
       "                      headers={'x-api-key': key},\n",
       "                      json={'payload': payload, 'assignment': no, 'ext': ext, 'py': encoded_python})\n",
       "    \n",
       "    # Handle the response\n",
       "    if r.status_code == 200:\n",
       "        print(\"Success: {}\".format(r.text))\n",
       "    else:\n",
       "        print(\"Failure: {}\".format(r.text))\n",
       "```\n",
       "\n",
       "This code is now well-commented and adheres to PEP-8 standards for better readability and maintainability."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Start a new conversation\n",
    "conversation = start_conversation()\n",
    "generate_code(conversation, \"\"\"\n",
    "Could you please explain what the following code does?\n",
    "\n",
    "import base64\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import PIL\n",
    "import PIL.Image\n",
    "import io\n",
    "\n",
    "# This function submits an assignment.  You can submit an assignment as much as you like, only the final\n",
    "# submission counts.  The paramaters are as follows:\n",
    "# data - List of pandas dataframes or images.\n",
    "# key - Your student key that was emailed to you.\n",
    "# no - The assignment class number, should be 1 through 1.\n",
    "# source_file - The full path to your Python or IPYNB file.  This must have \"_class1\" as part of its name.\n",
    "# .             The number must match your assignment number.  For example \"_class2\" for class assignment #2.\n",
    "def submit(data,key,no,source_file=None):\n",
    "    if source_file is None and '__file__' not in globals(): raise Exception('Must specify a filename when a Jupyter notebook.')\n",
    "    if source_file is None: source_file = __file__\n",
    "    suffix = '_class{}'.format(no)\n",
    "    if suffix not in source_file: raise Exception('{} must be part of the filename.'.format(suffix))\n",
    "    with open(source_file, \"rb\") as image_file:\n",
    "        encoded_python = base64.b64encode(image_file.read()).decode('ascii')\n",
    "    ext = os.path.splitext(source_file)[-1].lower()\n",
    "    if ext not in ['.ipynb','.py']: raise Exception(\"Source file is {} must be .py or .ipynb\".format(ext))\n",
    "    payload = []\n",
    "    for item in data:\n",
    "        if type(item) is PIL.Image.Image:\n",
    "            buffered = BytesIO()\n",
    "            item.save(buffered, format=\"PNG\")\n",
    "            payload.append({'PNG':base64.b64encode(buffered.getvalue()).decode('ascii')})\n",
    "        elif type(item) is pd.core.frame.DataFrame:\n",
    "            payload.append({'CSV':base64.b64encode(item.to_csv(index=False).encode('ascii')).decode(\"ascii\")})\n",
    "    r= requests.post(\"https://api.heatonresearch.com/assignment-submit\",\n",
    "        headers={'x-api-key':key}, json={ 'payload': payload,'assignment': no, 'ext':ext, 'py':encoded_python})\n",
    "    if r.status_code==200:\n",
    "        print(\"Success: {}\".format(r.text))\n",
    "    else: print(\"Failure: {}\".format(r.text))\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lob7vcRKgF0o"
   },
   "source": [
    "As you can see, the LLM explained my \"submit\" function.\n",
    "\n",
    "## Improving Code with LLMs\n",
    "\n",
    "You can also request that a LLM improve your code. You can mention specific improvements you seek, such as removing unused or redundant imports, sorting the imports, and adhering to PEP-8 for your code formatting. In the following code, I request that the LLM improve my submit function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "DV_rh0xpgIyZ",
    "outputId": "6af4a7c4-de6e-47b3-f352-6637b860a2fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model response:\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "Here are some improvements and notes on the provided code:\n",
       "\n",
       "1. **Import Optimization**: Group imports from the same library together.\n",
       "2. **Error Handling**: Add more specific error messages and handle potential exceptions that could occur during the request.\n",
       "3. **Code Readability**: Improve readability by breaking down complex lines into simpler statements and adding more comments.\n",
       "4. **Function Documentation**: Add a docstring to the function to explain its purpose, parameters, and expected behavior.\n",
       "5. **Constants**: Use uppercase for constants like the URL.\n",
       "6. **Resource Management**: Use `with` statement for handling `BytesIO` to ensure proper resource management.\n",
       "\n",
       "Here's the revised version of the code:\n",
       "\n",
       "```python\n",
       "import base64\n",
       "import io\n",
       "import os\n",
       "\n",
       "import numpy as np\n",
       "import pandas as pd\n",
       "import requests\n",
       "from PIL import Image\n",
       "\n",
       "# Constants\n",
       "SUBMISSION_URL = \"https://api.heatonresearch.com/assignment-submit\"\n",
       "\n",
       "def submit(data, key, no, source_file=None):\n",
       "    \"\"\"\n",
       "    Submit an assignment with given data, student key, and assignment number.\n",
       "    \n",
       "    Parameters:\n",
       "        data (list): List of pandas DataFrames or PIL Images to be submitted.\n",
       "        key (str): Student key for authentication.\n",
       "        no (int): Assignment number, should be a valid class number.\n",
       "        source_file (str, optional): Path to the source Python or IPYNB file.\n",
       "            If not provided, it attempts to use the current file.\n",
       "    \n",
       "    Raises:\n",
       "        Exception: If the source file is not specified in a Jupyter notebook environment.\n",
       "        Exception: If the source file name does not contain the correct class suffix.\n",
       "        Exception: If the source file extension is not .py or .ipynb.\n",
       "    \"\"\"\n",
       "    if source_file is None and '__file__' not in globals():\n",
       "        raise Exception('Must specify a filename when in a Jupyter notebook.')\n",
       "    if source_file is None:\n",
       "        source_file = __file__\n",
       "    \n",
       "    suffix = '_class{}'.format(no)\n",
       "    if suffix not in source_file:\n",
       "        raise Exception('{} must be part of the filename.'.format(suffix))\n",
       "    \n",
       "    with open(source_file, \"rb\") as image_file:\n",
       "        encoded_python = base64.b64encode(image_file.read()).decode('ascii')\n",
       "    \n",
       "    ext = os.path.splitext(source_file)[-1].lower()\n",
       "    if ext not in ['.ipynb', '.py']:\n",
       "        raise Exception(\"Source file extension {} must be .py or .ipynb\".format(ext))\n",
       "    \n",
       "    payload = []\n",
       "    for item in data:\n",
       "        if isinstance(item, Image.Image):\n",
       "            with io.BytesIO() as buffered:\n",
       "                item.save(buffered, format=\"PNG\")\n",
       "                payload.append({'PNG': base64.b64encode(buffered.getvalue()).decode('ascii')})\n",
       "        elif isinstance(item, pd.DataFrame):\n",
       "            payload.append({'CSV': base64.b64encode(item.to_csv(index=False).encode('ascii')).decode(\"ascii\")})\n",
       "    \n",
       "    response = requests.post(SUBMISSION_URL, headers={'x-api-key': key},\n",
       "                             json={'payload': payload, 'assignment': no, 'ext': ext, 'py': encoded_python})\n",
       "    \n",
       "    if response.status_code == 200:\n",
       "        print(\"Success: {}\".format(response.text))\n",
       "    else:\n",
       "        print(\"Failure: {}\".format(response.text))\n",
       "```\n",
       "\n",
       "### Execution Notes:\n",
       "- Ensure that the `requests` library is installed in your environment (`pip install requests`).\n",
       "- The function now uses `isinstance()` for type checking, which is generally preferred over direct type comparison.\n",
       "- The `BytesIO` object is managed using a `with` statement to ensure it is properly closed after its use."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conversation = start_conversation()\n",
    "generate_code(conversation, \"\"\"\n",
    "Could you please suggest and implement any improvements to the following code?\n",
    "\n",
    "import base64\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import PIL\n",
    "import PIL.Image\n",
    "import io\n",
    "\n",
    "# This function submits an assignment.  You can submit an assignment as much as you like, only the final\n",
    "# submission counts.  The paramaters are as follows:\n",
    "# data - List of pandas dataframes or images.\n",
    "# key - Your student key that was emailed to you.\n",
    "# no - The assignment class number, should be 1 through 1.\n",
    "# source_file - The full path to your Python or IPYNB file.  This must have \"_class1\" as part of its name.\n",
    "# .             The number must match your assignment number.  For example \"_class2\" for class assignment #2.\n",
    "def submit(data,key,no,source_file=None):\n",
    "    if source_file is None and '__file__' not in globals(): raise Exception('Must specify a filename when a Jupyter notebook.')\n",
    "    if source_file is None: source_file = __file__\n",
    "    suffix = '_class{}'.format(no)\n",
    "    if suffix not in source_file: raise Exception('{} must be part of the filename.'.format(suffix))\n",
    "    with open(source_file, \"rb\") as image_file:\n",
    "        encoded_python = base64.b64encode(image_file.read()).decode('ascii')\n",
    "    ext = os.path.splitext(source_file)[-1].lower()\n",
    "    if ext not in ['.ipynb','.py']: raise Exception(\"Source file is {} must be .py or .ipynb\".format(ext))\n",
    "    payload = []\n",
    "    for item in data:\n",
    "        if type(item) is PIL.Image.Image:\n",
    "            buffered = BytesIO()\n",
    "            item.save(buffered, format=\"PNG\")\n",
    "            payload.append({'PNG':base64.b64encode(buffered.getvalue()).decode('ascii')})\n",
    "        elif type(item) is pd.core.frame.DataFrame:\n",
    "            payload.append({'CSV':base64.b64encode(item.to_csv(index=False).encode('ascii')).decode(\"ascii\")})\n",
    "    r= requests.post(\"https://api.heatonresearch.com/assignment-submit\",\n",
    "        headers={'x-api-key':key}, json={ 'payload': payload,'assignment': no, 'ext':ext, 'py':encoded_python})\n",
    "    if r.status_code==200:\n",
    "        print(\"Success: {}\".format(r.text))\n",
    "    else: print(\"Failure: {}\".format(r.text))\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sNQv82Bzh4ao"
   },
   "source": [
    "As you can see, the LLM suggested several improvements that I will consider for future versions of this function."
   ]
  }
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